Magnetic resonance imaging -based radiomics of the pituitary gland is highly predictive of precocious puberty in girls: a pilot study

Front Endocrinol (Lausanne). 2025 Feb 5:16:1496554. doi: 10.3389/fendo.2025.1496554. eCollection 2025.

Abstract

Background: The aim of the study was to explore a radiomic model that could assist physicians in the diagnosis of central precocious puberty (CPP). A predictive model based on radiomic features (RFs), extracted form magnetic resonance imaging (MRI) of the pituitary gland, was thus developed to distinguish between CPP and control subjects.

Methods: 45 girls with confirmed diagnosis of CPP (CA:8.4 ± 0.9 yr) according to the current criteria and 47 age-matched pre-pubertal control subjects (CA:8.7 ± 1.2 yr) were retrospectively enrolled. Two readers (R1, R2) blindly segmented the pituitary gland on MRI studies for RFs and performed a manual estimation of the pituitary volume. Radiomics was compared against pituitary volume in terms of predictive performances (metrics: ROC-AUC, accuracy, sensitivity and specificity) and reliability (metric: intraclass correlation coefficient, ICC). Pearson correlation between RFs and auxological, biochemical, and ultrasound data was also computed.

Results: Two different radiomic parameters, Shape Surface Volume Ratio and Glrlm Gray Level Non-Uniformity, predicted CPP with a high diagnostic accuracy (ROC-AUC 0.81 ± 0.08) through the application of our ML algorithm. Anthropometric variables were not confounding factors of these RFs suggesting that premature thelarche and/or pubarche would not be potentially misclassified. The selected RFs correlated with baseline and peak LH (p < 0.05) after GnRH stimulation. The diagnostic sensitivity was improved compared to pituitary volume only (0.76 versus 0.68, p<0.001) and demonstrated higher inter-reader reliability (ICC>0.57 versus ICC=0.46).

Discussion: Radiomics is a promising tool to diagnose CPP as it reflects also functional aspects. Further studies are warranted to validate these preliminary data.

Keywords: central precocious puberty; machine learning; magnetic resonance imaging; pituitary gland; precocious puberty, puberty; radiomics.

MeSH terms

  • Child
  • Female
  • Humans
  • Magnetic Resonance Imaging* / methods
  • Pilot Projects
  • Pituitary Gland* / diagnostic imaging
  • Pituitary Gland* / pathology
  • Predictive Value of Tests
  • Puberty, Precocious* / diagnosis
  • Puberty, Precocious* / diagnostic imaging
  • Radiomics
  • Retrospective Studies

Grants and funding

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.